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| # | |
| # Copyright (C) 2023, Inria | |
| # GRAPHDECO research group, https://team.inria.fr/graphdeco | |
| # All rights reserved. | |
| # | |
| # This software is free for non-commercial, research and evaluation use | |
| # under the terms of the LICENSE.md file. | |
| # | |
| # For inquiries contact [email protected] | |
| # | |
| import torch | |
| def mse(img1, img2): | |
| return (((img1 - img2)) ** 2).view(img1.shape[0], -1).mean(1, keepdim=True) | |
| def psnr(img1, img2): | |
| mse = (((img1 - img2)) ** 2).view(img1.shape[0], -1).mean(1, keepdim=True) | |
| return 20 * torch.log10(1.0 / torch.sqrt(mse)) | |
| def masked_psnr(img1, img2, mask): | |
| mse = ((((img1 - img2)) ** 2) * mask).sum() / (3. * mask.sum()) | |
| return 20 * torch.log10(1.0 / torch.sqrt(mse)) | |
| def accuracy_torch(gt_points, rec_points, gt_normals=None, rec_normals=None, batch_size=5000): | |
| n_points = rec_points.shape[0] | |
| all_distances = [] | |
| all_indices = [] | |
| for i in range(0, n_points, batch_size): | |
| end_idx = min(i + batch_size, n_points) | |
| batch_points = rec_points[i:end_idx] | |
| distances = torch.cdist(batch_points, gt_points) # (batch_size, M) | |
| batch_distances, batch_indices = torch.min(distances, dim=1) # (batch_size,) | |
| all_distances.append(batch_distances) | |
| all_indices.append(batch_indices) | |
| distances = torch.cat(all_distances) | |
| indices = torch.cat(all_indices) | |
| acc = torch.mean(distances) | |
| acc_median = torch.median(distances) | |
| if gt_normals is not None and rec_normals is not None: | |
| normal_dot = torch.sum(gt_normals[indices] * rec_normals, dim=-1) | |
| normal_dot = torch.abs(normal_dot) | |
| return acc, acc_median, torch.mean(normal_dot), torch.median(normal_dot) | |
| return acc, acc_median | |
| def completion_torch(gt_points, rec_points, gt_normals=None, rec_normals=None, batch_size=5000): | |
| n_points = gt_points.shape[0] | |
| all_distances = [] | |
| all_indices = [] | |
| for i in range(0, n_points, batch_size): | |
| end_idx = min(i + batch_size, n_points) | |
| batch_points = gt_points[i:end_idx] | |
| distances = torch.cdist(batch_points, rec_points) # (batch_size, M) | |
| batch_distances, batch_indices = torch.min(distances, dim=1) # (batch_size,) | |
| all_distances.append(batch_distances) | |
| all_indices.append(batch_indices) | |
| distances = torch.cat(all_distances) | |
| indices = torch.cat(all_indices) | |
| comp = torch.mean(distances) | |
| comp_median = torch.median(distances) | |
| if gt_normals is not None and rec_normals is not None: | |
| normal_dot = torch.sum(gt_normals * rec_normals[indices], dim=-1) | |
| normal_dot = torch.abs(normal_dot) | |
| return comp, comp_median, torch.mean(normal_dot), torch.median(normal_dot) | |
| return comp, comp_median | |
| def accuracy_per_point(gt_points, rec_points, batch_size=5000): | |
| n_points = rec_points.shape[0] | |
| all_distances = [] | |
| all_indices = [] | |
| for i in range(0, n_points, batch_size): | |
| end_idx = min(i + batch_size, n_points) | |
| batch_points = rec_points[i:end_idx] | |
| distances = torch.cdist(batch_points, gt_points) # (batch_size, M) | |
| batch_distances, batch_indices = torch.min(distances, dim=1) # (batch_size,) | |
| all_distances.append(batch_distances) | |
| all_indices.append(batch_indices) | |
| distances = torch.cat(all_distances) | |
| return distances | |
| def completion_per_point(gt_points, rec_points, batch_size=5000): | |
| n_points = gt_points.shape[0] | |
| all_distances = [] | |
| all_indices = [] | |
| for i in range(0, n_points, batch_size): | |
| end_idx = min(i + batch_size, n_points) | |
| batch_points = gt_points[i:end_idx] | |
| distances = torch.cdist(batch_points, rec_points) # (batch_size, M) | |
| batch_distances, batch_indices = torch.min(distances, dim=1) # (batch_size,) | |
| all_distances.append(batch_distances) | |
| all_indices.append(batch_indices) | |
| distances = torch.cat(all_distances) | |
| return distances |